RRepoGEO

REPOGEO REPORT · LITE

tkarim45/Beginner-Data-Science-Projects

Default branch main · commit aa293a66 · scanned 5/23/2026, 9:43:09 PM

GitHub: 2,170 stars · 430 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface tkarim45/Beginner-Data-Science-Projects, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to emphasize direct project collection

    Why:

    CURRENT
    A curated collection of beginner-friendly data science projects with real datasets, clear explanations, and working code. Learn by building.
    COPY-PASTE FIX
    This repository is a **direct, ready-to-run collection of hands-on data science projects** tailored for beginners. Each project is a standalone Jupyter notebook with real datasets, clear explanations, and working code, designed for immediate application and learning by building.
  • mediumreadme#2
    Explicitly mention repo name in 'Why This Repo?' section

    Why:

    CURRENT
    This repository is designed for **anyone getting started with data sciencestudents, career switchers, and self-learners. Each project is a standalone Jupyter notebook that you can clone and run immediately.
    COPY-PASTE FIX
    This **Beginner-Data-Science-Projects** repository is designed for **anyone getting started with data science**—students, career switchers, and self-learners. Each project is a standalone Jupyter notebook that you can clone and run immediately.
  • lowhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/tkarim45/Beginner-Data-Science-Projects

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface tkarim45/Beginner-Data-Science-Projects
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 1×
  2. DataCamp Projects · recommended 1×
  3. Coursera · recommended 1×
  4. edX · recommended 1×
  5. Towards Data Science · recommended 1×
  • CATEGORY QUERY
    Where can I find practical machine learning projects to start learning data science?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. DataCamp Projects
    3. Coursera
    4. edX
    5. Towards Data Science
    6. Medium
    7. GitHub
    8. Awesome Machine Learning

    AI recommended 8 alternatives but never named tkarim45/Beginner-Data-Science-Projects. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some good hands-on deep learning projects for someone new to AI?
    you: not recommended
    AI recommended (in order):
    1. Keras (keras-team/keras)
    2. TensorFlow (tensorflow/tensorflow)
    3. PyTorch (pytorch/pytorch)
    4. YOLO (You Only Look Once)
    5. SSD (Single Shot MultiBox Detector)
    6. Hugging Face Transformers (huggingface/transformers)

    AI recommended 6 alternatives but never named tkarim45/Beginner-Data-Science-Projects. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of tkarim45/Beginner-Data-Science-Projects?
    pass
    AI did not name tkarim45/Beginner-Data-Science-Projects — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts tkarim45/Beginner-Data-Science-Projects in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tkarim45/Beginner-Data-Science-Projects explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo tkarim45/Beginner-Data-Science-Projects solve, and who is the primary audience?
    pass
    AI did not name tkarim45/Beginner-Data-Science-Projects — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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